Brain Tumor Detection from MRI Using Deep Learning

Abstract

Brain tumors represent one of the most critical neurological disorders, where early and accurate diagnosis is essential for improving patient survival and treatment outcomes. Magnetic Resonance Imaging (MRI) remains the preferred modality for detection due to its high resolution and non-invasive nature. However, manual interpretation of MRI scans is time-intensive, requires specialized expertise, and is prone to variability, particularly when tumor boundaries are indistinct. This study proposes an automated brain tumor detection and segmentation system leveraging deep learning techniques. The framework integrates EfficientNet for tumor classification and U-Net++ for pixel-level segmentation. MRI images undergo preprocessing steps—resizing, normalization, and noise reduction—to ensure consistency and enhance quality. EfficientNet then extracts high-level features to classify scans into tumor and non-tumor categories with high accuracy. Upon detection, U-Net++ performs precise segmentation, utilizing its nested encoder–decoder architecture and dense skip connections to achieve improved boundary delineation. The system is implemented using Python-based deep learning frameworks, enabling efficient model integration and rapid processing. By reducing reliance on manual interpretation, the proposed solution enhances diagnostic accuracy, minimizes analysis time, and supports early-stage tumor detection. This work demonstrates the practical application of artificial intelligence in medical imaging, offering reliable diagnostic support for radiologists and healthcare institutions.

Introduction

The text presents an automated brain tumor detection and segmentation system using deep learning to improve early diagnosis, accuracy, and efficiency in medical imaging. Brain tumors, caused by abnormal cell growth in the brain, pose high risks due to their impact on cognitive and motor functions. Early detection via MRI is crucial, but manual analysis is time-consuming, error-prone, and highly dependent on radiologist expertise.

Key components of the proposed system:

• MRI Image Acquisition: Collects diverse tumor and non-tumor scans, including glioma, meningioma, and pituitary tumors.

• Preprocessing: Resizing, noise reduction, intensity normalization, and data augmentation improve image quality and model robustness.

• Tumor Classification (EfficientNet): Identifies presence of tumors using a scalable and efficient deep learning model, leveraging pre-trained weights for faster convergence and high accuracy (~95%).

• Tumor Segmentation (U-Net++): Precisely localizes tumor regions with nested encoder-decoder architecture and dense skip connections, producing accurate pixel-level masks.

• Training & Optimization: Uses categorical cross-entropy for classification, Dice/IoU loss for segmentation, and Adam optimizer with learning rate scheduling to ensure reliable training.

• Performance Evaluation: Metrics include accuracy, precision, recall, F1-score for classification, and Dice coefficient and IoU for segmentation.

Results:

• EfficientNet reliably distinguishes tumor vs. non-tumor MRI scans with high accuracy and low false positives/negatives.

• U-Net++ achieves precise segmentation of tumor boundaries, preserving spatial information.

• Combined classification and segmentation outperform models focusing on only one task, enabling early detection and effective visualization of tumor regions.

Significance:
The system reduces radiologist workload, minimizes human error, and supports clinical decision-making by providing automated, interpretable, and high-precision tumor detection and localization. It demonstrates the practical application of AI in enhancing medical diagnostics.

Conclusion

The proposed brain tumor detection system demonstrates that deep learning techniques can be effectively applied to analyze MRI brain images. By combining EfficientNet for tumor classification and U-Net++ for precise segmentation, the system accurately identifies the presence of brain tumors and highlights affected regions. MRI images can be processed automatically with minimal human intervention, making the system practical and efficient for clinical use. Overall, the proposed approach provides a reliable and automated tool that can assist radiologists in early diagnosis, improve diagnostic accuracy, and support better treatment planning in healthcare environments.

References

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Copyright

Copyright © 2026 Suryakumar N, Dr. K. Santhi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.